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Found 5,666 Skills
2-layer parallel agent hierarchy. Layer 1 deploys 3-50+ agents, each with independent context. Layer 2 adds 2+ sub-agents per member. No upper limit on either layer.
Create a new runbook with guided assistance. A runbook is a structured markdown document that tells a coding agent how to accomplish a complex, multi-step task with evaluation loops and quality gates. Use this skill whenever the user wants to create, build, scaffold, or write a runbook — including 'create runbook', 'new runbook', 'build a runbook', 'make a runbook', 'runbook wizard', 'help me write a runbook', 'I need a runbook for...', 'automate this task with a runbook', or 'turn this into a runbook'. Also trigger when the user describes a multi-step agent task that would benefit from structured evaluation and iteration loops, even if they don't use the word 'runbook' — for example, 'I want to build an automated pipeline that evaluates its own output' or 'create a repeatable process with quality gates'.
The foundational context engineering skill — start here when exploring the discipline. This skill should be used when the user asks to "understand context", "explain context windows", "design agent architecture", "debug context issues", "optimize context usage", or discusses context components, attention mechanics, progressive disclosure, or context budgeting. Also activates when the user mentions "context engineering" or "context-engineering" for foundational understanding of AI agent context systems.
Build MCP (Model Context Protocol) servers including tool definition, schema design, authentication, error handling, and Claude Code integration. Use this skill when the user needs to create an MCP server, expose APIs or databases to AI agents, design tool schemas, or integrate with Claude Code — even if they say 'build an MCP server', 'connect Claude to our database', 'expose our API to AI', or 'create a tool for Claude Code'.
Artifact status + multi-phase orchestration. Scan what exists, check freshness, compose and track complex workflows across sessions. Not for skill routing (the agent does that proactively).
Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
Periodic self-monitoring and health check system for autonomous agents. Runs scheduled health diagnostics, reports system status, and performs proactive maintenance tasks.
Guide for creating, improving, benchmarking, and packaging Claude Agent Skills (SKILL.md files). Invoke when users want to create a skill from scratch, improve or test an existing skill, benchmark skill performance with variance analysis, or optimize a skill description for triggering accuracy. Also invoke when users say "turn this into a skill", "make a skill for X", "help me write a SKILL.md", "my skill isn't firing correctly", or want to convert a workflow/conversation into a reusable skill. Invoke proactively when a conversation has produced a repeatable workflow worth capturing. If the user mentions SKILL.md, skill files, skill descriptions, or skill triggering, this skill applies.
Decomposes complex, multi-day tasks into optimized milestones using parallel reviewer agents (ultraplan). Spawns 5 independent reviewers that analyze the problem from different angles, then synthesizes their findings into a milestone dependency DAG. Triggers when the user says "plan milestones", "break this into milestones", "ultraplan", or when long-run harness needs milestone generation.
Umbrella skill for agent work discipline across development, analysis, and documentation: inspect the repo before restructuring, keep durable truth in repo artifacts instead of chat memory, co-evolve specs/design/steering/user docs with code, apply sound coding patterns, verify work honestly, avoid shortcuts, work efficiently with subagents without hallucinating, and keep moving through the next concrete work item when the human is away. References cover coding patterns, AI-authored code review, and artifact co-evolution. Trigger when the user asks for workflow discipline, coding patterns, doc/artifact maintenance, code review of AI-authored code, project hygiene, execution guardrails, repo normalization, or when a task risks drifting across architecture, storage, specs, continuity, or tooling boundaries.
Use when building features that answer questions from private data, documents, policies, or time-sensitive information — RAG architecture, chunking strategies, hybrid search, re-ranking, vector databases, evaluation, agentic RAG, multimodal RAG...
Creates agent-optimized technical design documents with context-layer-aware progressive disclosure for architecture decisions, component design, and data models. Use when writing technical designs, architecture docs, defining system components, or making technology choices for spec-driven development.